Scam Detection & Prevention Solutions

Stop Scam Losses, Build Customer Trust, Protect Revenue

Identify signs of customer manipulation and coercion with Feedzai’s ScamPrevent™. Elevate your scam prevention.

Embed Proven Strategies to Stop Scams

Analyze scam risk from all angles using transactional, behavioral, device, and network intelligence with built-in scam strategies.

Trusted by global financial leaders

Catch Scams, Stop Losses, Protect Customers

Catch More Scams

Prevent authorized scam losses by monitoring customer behavior for suspicious activity.

Out-of-the-Box Models

Access scam detection models trained on large volumes of historical data to detect scam activity and reduce losses.

Silent Customer Protection

Keep your customers safe from making authorized fraud transfers without disrupting their journey.

Stay Ahead of Changing Scam Threats

Deploy Battle-tested Detection Models

Access ready-to-use best-practice scam detection models and rules trained using hundreds of features from known scam activity designed to reduce financial losses

Customize Models to Your Needs

Craft score thresholds and workflows best-suited to your unique needs. Perform rule simulation, model validation, and comparison in days, not weeks, before model changes are deployed.

3-in-1 Silent Customer Protection

Authenticate every interaction and transaction in real-time, using a combination of transactional analytics, behavioral biometrics, and device intelligence—without disrupting the customer journey.

Empower Customers to Stop Scams Themselves

Equip your customers with the ability to assess the legitimacy of a payment request that starts outside of the banking app. Feedzai’s new GenAI agent, ScamAlert, makes it simple.

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One Unified Platform for Comprehensive and Cost-Effective Scam Prevention

Use out-of-the-box risk strategies to detect use of automation tools and bots. Eliminate unnecessary third-party identity verification costs downstream.

Continuously review behavioral, device, and threat data combined with activity and transactional information to update risk levels and respond to customer duress.

Quickly react to new threats by identifying patterns with less time spent effort adjusting models by analysts.

Access lessons from multiple customers to prevent a wide range of impersonation and misrepresentation scams.

"[Feedzai] helped us uncover and fight new patterns that would either have gone undetected or been much harder to effectively block.”

— Duarte Pupo Correia, Head of Direct Sales and Service Department at Novobanco.

Feedzai IQ™ Defends Banks Against AI-Driven Fraud

Feedzai’s ScamPrevent Tackles Cross-Channel Scams for Major EU Bank

70% fraud detection rate

0.08% of total events blocked for fraud

12:1 false positive detection rate

Frequently Asked Questions

Feedzai’s scam detection uses behavioral biometrics, real-time transaction analysis to identify coercion or suspicious activity. It monitors user sessions, flags anomalies, and triggers real-time interventions like step-up authentication or payment delays. This helps stop scams before funds leave the victim’s account.

Feedzai monitors active calls during incoming and outgoing payments, identifies Remote Access Tools (RATs), tracks unusually long sessions, and uses Active Defense to block risky activity. DT uses behavior analysis, device scanning, and session timing to spot scams. Active Defense can end sessions automatically to protect users during suspicious behavior.

Feedzai’s AI leverages transactional, biometric, and other data to build a 360-degree baseline of each customer’s banking behavior, enabling the most accurate identification of anomalies and scam related behaviour. Feedzai also offers Feedzai IQ TrustScore, an AI-powered risk score that leverages real-time, network-wide intelligence to assess scams.

Feedzai enables FIs to intervene based on risk signals. Using these, FIs can configure in-session alerts during risky transactions, communicate messages to educate users about common scams. It may trigger step-up authentication or delay transactions to prevent losses. Interventions are personalized based on behavior, disrupting scams before money is sent.

Scam prevention focuses on stopping fraud where the victim is tricked into authorizing the payment, often through social engineering. Traditional fraud detection targets unauthorized transactions by third parties. Scam prevention requires behavioral analysis and real-time user engagement, while traditional fraud relies more on pattern recognition.